Prithvijit Chattopadhyay
Prithvijit Chattopadhyay
Ph.D. Student in CS, Georgia Tech
Verified email at - Homepage
Cited by
Cited by
Learning to balance specificity and invariance for in and out of domain generalization
P Chattopadhyay, Y Balaji, J Hoffman
Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020
Counting everyday objects in everyday scenes
P Chattopadhyay, R Vedantam, RR Selvaraju, D Batra, D Parikh
Proceedings of the IEEE conference on computer vision and pattern …, 2017
Do explanations make VQA models more predictable to a human?
A Chandrasekaran, V Prabhu, D Yadav, P Chattopadhyay, D Parikh
arXiv preprint arXiv:1810.12366, 2018
Evaluating visual conversational agents via cooperative human-ai games
P Chattopadhyay, D Yadav, V Prabhu, A Chandrasekaran, A Das, S Lee, ...
Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 5 …, 2017
It takes two to tango: Towards theory of AI's mind
A Chandrasekaran, D Yadav, P Chattopadhyay, V Prabhu, D Parikh
arXiv preprint arXiv:1704.00717, 2017
Choose your neuron: Incorporating domain knowledge through neuron-importance
RR Selvaraju, P Chattopadhyay, M Elhoseiny, T Sharma, D Batra, ...
Proceedings of the European conference on computer vision (ECCV), 526-541, 2018
Evalai: Towards better evaluation systems for ai agents
D Yadav, R Jain, H Agrawal, P Chattopadhyay, T Singh, A Jain, SB Singh, ...
arXiv preprint arXiv:1902.03570, 2019
Robustnav: Towards benchmarking robustness in embodied navigation
P Chattopadhyay, J Hoffman, R Mottaghi, A Kembhavi
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021
Improving generative visual dialog by answering diverse questions
V Murahari, P Chattopadhyay, D Batra, D Parikh, A Das
arXiv preprint arXiv:1909.10470, 2019
Battle of the backbones: A large-scale comparison of pretrained models across computer vision tasks
M Goldblum, H Souri, R Ni, M Shu, V Prabhu, G Somepalli, ...
Advances in Neural Information Processing Systems 36, 2024
Lance: Stress-testing visual models by generating language-guided counterfactual images
V Prabhu, S Yenamandra, P Chattopadhyay, J Hoffman
Advances in Neural Information Processing Systems 36, 25165-25184, 2023
Pasta: Proportional amplitude spectrum training augmentation for syn-to-real domain generalization
P Chattopadhyay, K Sarangmath, V Vijaykumar, J Hoffman
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023
IR-VIC: Unsupervised Discovery of Sub-goals for Transfer in RL
N Modhe, P Chattopadhyay, M Sharma, A Das, D Parikh, D Batra, ...
Benchmarking Low-Shot Robustness to Natural Distribution Shifts
A Singh, K Sarangmath, P Chattopadhyay, J Hoffman
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023
Likelihood landscapes: A unifying principle behind many adversarial defenses
F Lin, R Mittapalli, P Chattopadhyay, D Bolya, J Hoffman
Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020 …, 2020
We're Not Using Videos Effectively: An Updated Domain Adaptive Video Segmentation Baseline
S Kareer, V Vijaykumar, H Maheshwari, P Chattopadhyay, J Hoffman, ...
arXiv preprint arXiv:2402.00868, 2024
AUGCAL: Improving Sim2Real Adaptation by Uncertainty Calibration on Augmented Synthetic Images
P Chattopadhyay, B Goyal, B Ecsedi, V Prabhu, J Hoffman
International Conference on Learning Representations, 2024
SkyScenes: A Synthetic Dataset for Aerial Scene Understanding
S Khose, A Pal, A Agarwal, J Hoffman, P Chattopadhyay
arXiv preprint arXiv:2312.06719, 2023
Augmentation Curriculum Learning For Generalization in RL
D Yung, A Szot, P Chattopadhyay, J Hoffman, Z Kira
Exploring Weak-Supervision and Generative Models for Semantic Segmentation
P Chattopadhyay, R Selvaraju, V Prabhu
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